针对RX算法中局部背景协方差矩阵估计的局限性，提出一种改进的RX (I-RX)异常检测算法。基于奇异值分解(SVD)，将高光谱图像投影到背景的正交子空间上，获得仅包含噪声和异常的残留图像。在此基础上，通过计算各样本的空间秩深度将残留图像划分为噪声背景和潜在异常两个样本集，利用噪声背景集估计整幅图像的背景协方差矩阵，并将潜在异常集作为测试样本进行异常检测。对模拟数据和真实高光谱数据进行了实验仿真，ROC曲线表明，在相同的虚警概率下，I-RX算法的检测概率相较于RX平均提高了2倍左右。

Aiming to reduce the limitation in local background covariance matrix estimation of RX algorithm，an improved RX (I-RX) algorithm is proposed for anomaly detection in hyperspectral imagery.Based on a singular value decomposition (SVD)，We firstly project the hyperspectral imagery onto the background orthogonal subspace to obtain the remaining imagery which only consists of noisy background and anomaly.On this basis，by calculating the spatial rank depth value of every sample，the remaining imagery can be divided into two sample sets：noise background set and potential anomaly set.Using the noise background set to estimate the background covariance matrix of the whole imagery and the potential anomaly set as test examples to be detected whether has anomaly or not.Numerical experiments are performed on simulated data and real hyperspectral data.The ROC curves demonstrate that the detection probability of I-RX algorithm is about 2 times than RX algorithm at the same false alarm rates.